Krishna Sai Vootla
Verified Expert in Engineering
Machine Learning Developer
Bengaluru, Karnataka, India
Toptal member since October 31, 2019
Krishna is a GenAI product architect and ML engineer with 7+ years of experience delivering AI solutions across marketing, retail, and analytics. He designs end-to-end GenAI products using LLMs, RAG, embeddings, and agentic workflows. At The Weather Company, he led the development of a weather-aware strategy assistant enabling what-if simulations and automated briefings. Krishna combines business analysis with deep ML and LLMOps expertise to build impactful enterprise AI systems.
Portfolio
Experience
- Machine Learning - 7 years
- Python - 6 years
- Deep Learning - 5 years
- Natural Language Processing (NLP) - 4 years
- LangChain - 2 years
- AI Chatbots - 2 years
- Large Language Models (LLMs) - 2 years
- OpenAI - 1 year
Work Experience
GenAI Product Architect | Senior Data Scientist
The Weather Company
- Produced RAG on AWS Bedrock + OpenSearch by parent-child chunking, hybrid search (BM25+dense) with RRF, Cohere cross-encoder reranking, HyDE, query decomposition, citation grounding. Langfuse + RAGAS evals, semantic caching, token streaming.
- Architected and led the end-to-end design and delivery of a GenAI marketing assistant using an agentic LLM framework to summarize weather impacts, recommend region-season strategies, simulate what-if scenarios, and generate strategic briefings.
- Collaborated with product, data science, and engineering teams to align GenAI workflows with business priorities, building reusable LLM components and simulation tools.
- Led technical strategy/implementation of organization-wide user embeddings platform using temporal graph neural networks (GNN), generating user embeddings for 100+ million users, powering personalization, recommendation systems, and user analytics.
- Spearheaded the development of an enterprise-wide self-supervised learning pipeline leveraging unlabeled data to enable modeling in label-scarce environments and expanding machine learning (ML) capabilities to previously unmodelable user segments.
- Led the development of an automated model explainability framework leveraging SHAP, PDP, and ICE plots, establishing ML governance standards across teams that streamlined model validation processes and accelerated model adoption across product teams.
AI Solution Architect
Syngenta - Syngenta AG
- Led end-to-end improvements to enterprise knowledge-base chat systems across Brazil and US domains, strengthening answer reliability through better retrieval architecture using Agentic orchestration, data alignment, and runtime decisioning.
- Designed and built RAG observability and traceability systems.
- Drove production-readiness from development to rollout by implementing robust data synchronization, observability, and safe deployment patterns for high-impact AI-assisted workflows.
Data Scientist | Full-stack AI Engineer
Organifi
- Built a SAC-based dynamic pricing engine to stop margin loss and expiry waste on perishable SKUs; trained on 18-mo demand with guardrails—pilot raised gross margin and cut stock-outs in targeted cohorts.
- Deployed a DQN checkout upsell to fix stagnant attach rates; action-masking and DR-OPE ensured UX safety—50/50 A/B increased AOV with no conversion drop and lower offer fatigue.
- Collaborated with marketing leadership to develop data-driven performance measurement frameworks, optimizing campaign ROAS by 18% and guiding $5+ million budget allocation.
- Development of company-wide churn analytics, including propensity models, lifecycle triggers, and RFM customer segmentation frameworks, that helped implement targeted retention campaigns, reducing churn from 20% to 14% and increasing customer LTV.
- Developed advanced forecasting models, including seasonality-adjusted SARIMAX for inventory and revenue prediction, contributing to a 35% reduction in stock-outs and improved supply chain management.
- Led redesign of company-wide experimentation framework to address critical flaws in metric interactions, preventing $5-7 million phantom revenue. Established org-wide A/B testing standards, guardrails, and authored playbooks adopted across product teams.
- Built data pipelines in AWS and GCP for a reporting and analytics data warehouse.
- Contributed to designing a centralized OLAP data warehouse with efficient dimensional modeling and took ownership of ETL processes for custom metrics and complex transformations. Implemented cost optimizations that reduced overall scan costs by 25%.
Back-end Data Engineer
Multithread
- Developed an end-to-end ETL pipeline from scratch to load data into a graph database and relational database.
- Built back-end APIs to create customized email text for reaching out to leads using OpenAI API.
- Created back-end APIs to manipulate and manage data in the graph database.
Analyst
JP Morgan Chase & Co
- Designed and built a next-generation merchant acquisition tool in R Shiny for a credit card business.
- Provided pricing analysis of credit card business.
- Built and integrated a minimum revenue model based on customer demographics.
Business Analyst
Tredence Analytics
- Partnered with cross-functional teams to prototype dashboards and deliver actionable insights that shaped roadmap-level retail initiatives.
- Segmented retail customers based on their shopping behavior using Random Forest.
- Designed, built, and deployed an end-to-end ML pipeline for customer segmentation using Random Forest, enabling targeted marketing strategies.
- Conducted marketing performance analysis for a leading US retailer, influencing product campaign decisions and strategic planning.
Software Analyst
Capgemini
- Scraped the web for collecting unstructured data present on a website.
- Created and deployed various executive summary dashboards.
- Automated data cleaning pipelines to save significant person-hours every week.
Project History
LLM Chatbot for Interacting with Documents
Users can use this application to achieve the following:
1. AI-powered summarization
2. Interactive Q&A
GitHub Link: https://github.com/krishnasaivootla/ChatWithDocs/blob/main/streamlit_app.py
AnalyticsGPT: LLM-based Data Analysis
At the core of AnalyticsGPT is its LLM, which understands and processes complex data patterns and trends. Users can interact with their data through natural language queries, making data analysis more accessible and less time-consuming. Whether you're looking to identify key trends, predict future patterns, or simply explore your data, AnalyticsGPT offers a user-friendly platform that caters to both seasoned analysts and those new to data science.
GitHub Link: https://github.com/krishnasaivootla/AnalyticsGPT/tree/main
Multi-modal Fully Convolutional Network for Semantic Segmentation
https://github.com/prml615/prmlThe project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes.
I used Intel Edison and Microsoft Kinect for proof of concept and prototype creation.
Smart Medical Network
AI Puppy Influencer
Education
Bachelor of Technology Degree in Electrical Engineering
Indian Institute of Technology Gandhinagar - Gandhinagar, India
Certifications
Statistical Learning
Stanford Online
Sentiment Analysis in Python
DataCamp
Building Web Applications in R with Shiny: Case Studies
DataCamp
Building Web Applications in R with Shiny
DataCamp
CodeChef Certified Data Structure & Algorithms Programme
CodeChef
Intermediate R
DataCamp
Data Manipulation in R with dplyr
DataCamp
Introduction to R
DataCamp
Python A-Z: Python for Data Science with Real Exercises!
Udemy
SQL - MySQL for Data Analytics & Business Intelligence
Udemy
Structuring Machine Learning Projects
Coursera
Skills
Libraries/APIs
PyTorch, Keras, NumPy, Pandas, Matplotlib, Ggplot2, Scikit-learn, OpenCV, TensorFlow, OpenAI API, Tidyverse, Beautiful Soup, Standard Template Library (STL), SciPy
Tools
Jira, Git, Tableau, Dplyr, Scikit-image, Looker, AWS Glue, Terminal, ComfyUI, Microsoft Power BI, Amazon ElastiCache, PyCharm, Jupyter, Spyder, BigQuery, Tableau Desktop Pro, ChatGPT, Amazon SageMaker, Claude Code
Languages
SQL, Python, Python 3, R, Snowflake, Bash Script, C++, C, Embedded C
Frameworks
Agentic Frameworks, RStudio Shiny, Spark, Microsoft Kinect, Flask, LlamaIndex
Platforms
Google Cloud Platform (GCP), Amazon Web Services (AWS), Docker, LangSmith, RStudio, Linux, Oracle, Arduino, Raspberry Pi, Raspberry Pi 3 GPIO, Jupyter Notebook, Anaconda, AWS Lambda, Azure, Databricks
Paradigms
Agile, Asynchronous Programming
Storage
MySQL, Databases, Data Lakes, Redis, Google Cloud, Google Cloud Storage, Neo4j
Industry Expertise
Applied Statistics
Other
Freelancing, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Data Science, Artificial Intelligence (AI), Technical Hiring, Code Review, Source Code Review, Large Language Models (LLMs), AI Chatbots, Generative Artificial Intelligence (GenAI), Retrieval-augmented Generation (RAG), AI Agents, FAISS, Pinecone, Reinforcement Learning, Modeling, Statistics, Agentic AI, Deep Reinforcement Learning, Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Large Language Models (LLMs), Natural Language Processing (NLP), Algorithms, Neural Networks, Deep Neural Networks (DNNs), Data Analytics, Data Reporting, Exploratory Data Analysis, Statistical Data Analysis, Statistical Learning, Statistical Modeling, Analytics, Predictive Analytics, Statistical Analysis, Data Analysis, Artificial Neural Networks (ANN), Interviewing, Task Analysis, Generative Pre-trained Transformers (GPT), Chatbots, Minimum Viable Product (MVP), APIs, Data Modeling, LangChain, Scalable Vector Databases, OpenAI GPT-4 API, Open-source LLMs, Team Leadership, Llama 3, Mistral AI, Prompt Engineering, Multi-agent Systems, Vector Search, Clustering, Clustering Algorithms, DBSCAN, Hierarchical Clustering, K-means Clustering, FastAPI, Bayesian Inference & Modeling, AI Design, Document Parsing, AI Programming, Back-end Development, Recommendation Systems, Fivetran, Data Build Tool (dbt), Data Engineering, Forecasting, Statistical Methods, AI Product Management, Retrieval-augmented Generation (RAG), Product Management, TensorFlow, PyTorch, Retail Technology, Machine Learning Operations (MLOps), Anthropic, Vector Databases, Cloud, Artificial Intelligence as a Service (AIaaS), RAG Architecture, Technical Writing, RAG Systems, Quantitative Analysis, Sentiment Analysis, Google Cloud Functions, Amazon RDS, Chatbot Conversation Design, OpenAI, Stable Diffusion, Image to Video, Text to Video, AI Content Creation, Kling AI, Text to Image AI, Image to Text, Text to Image, Fine-tuning, Docker, Kubernetes, AI Product Management
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